In this paper, we present a new, biologically inspired perceptual
feature to solve the selectivity and invariance issue in object recognition.
Based on the recent findings in neuronal and cognitive mechanisms
in human visual systems, we develop a computationally efficient model.
An effective form of a visual part detector combines a radial symmetry
detector with a corner-like structure detector. A general context descriptor
encodes edge orientation, edge density, and hue information using a
localized receptive field histogram. We compare the proposed perceptual
feature (G-RIF: generalized robust invariant feature) with the state-ofthe-
art feature, SIFT, for feature-based object recognition. The experimental
results validate the robustness of the proposed perceptual feature
in object recognition.
feature to solve the selectivity and invariance issue in object recognition.
Based on the recent findings in neuronal and cognitive mechanisms
in human visual systems, we develop a computationally efficient model.
An effective form of a visual part detector combines a radial symmetry
detector with a corner-like structure detector. A general context descriptor
encodes edge orientation, edge density, and hue information using a
localized receptive field histogram. We compare the proposed perceptual
feature (G-RIF: generalized robust invariant feature) with the state-ofthe-
art feature, SIFT, for feature-based object recognition. The experimental
results validate the robustness of the proposed perceptual feature
in object recognition.